Comparison of 3-km analyses and forecasts from WRF-LETKF and WRF-

EAKF ensembles

Corey Potvin1,2, Dusty Wheatley1,2, Kent Knopfmeier1,2, Lou Wicker2, and Terra Ladwig3

1Cooperative Institute for Mesoscale Meteorological Studies, Norman, OK2NOAA National Severe Storms Lab, Norman, OK

3NOAA ESRL/Global Sciences Division and CIRES/University of Colorado, Boulder, CO

NOAA Warn-on-Forecast

• Paradigm shift in short-term forecasting of convective hazards: NWP plays major role– tornadoes, heavy rain, damaging wind/hail, etc.

• Convection-allowing ensemble data assimilation & forecasts – provide probabilistic information

• Primary goal: extend warning lead times

• Conventional + radar + satellite data– assimilated with EnKF or VAR-EnKF hybrid

LETKF vs. serial filters

• LETKF: Local Ensemble Transform Kalman Filter (Hunt et al. 2007)

• EAKF: Ensemble Adjustment Kalman Filter (Anderson 2001)

• Primary difference: LETKF processes observations simultaneously, allowing grid points to be updated in parallel

• This may allow more efficient scaling to many CPUs (Miyoshi and Yamane 2007)

• Weight interpolation may allow analysis to be performed on coarser grid (Yang et al. 2009)

LETKF vs. serial filters (cont.)

• Unclear a priori which filter type (if either) more accurate on convection-allowing scales

• Interaction between serial processing and covariance localization can degrade updates (Nerger 2015)

• But, given nonlinear obs operators, serial processing can improve updates during spin-up (Zhao et al. 2013)

• LETKF better preserves mass balance in analyses/forecasts (e.g., Holland and Wang 2013)

Goal of present study

• If LETKF competitive with serial filters for storm-scale ensembles, then we should explore computational advantages

• Thompson et al. (2015, QJRMS) found LETKF to be competitive with EnSRF in OSSEs and a real-data case with a cloud model (NCOMMAS)

• We now extend comparisons to full-physics model (WRF) with real data

General approach

• Use WRF-LETKF configured similarly to NSSL Experimental WoF System ensemble (NEWS-e), which uses WRF-DART (WRF-EAKF)

• Compare analyses and (esp.) forecasts from these two systems

• Three tornadic + non-tornadic supercell cases:– 19 May 2013, 20 May 2013 (Oklahoma) – 27 Apr 2014 (Arkansas; not shown)

NEWS-e grid configuration

Δx = 15 km

Δx = 3 km

3-km ensemble settings

• IC/BC from NEWS-e 3-km nest

• WRF-ARW 3.6.1

• 36 members, Thompson microphysics, radiation/PBL/surface physics diversity

• 15-min cycles: 3 88D’s (Vr & dBZ), OK mesonet

• Additive noise (Dowell & Wicker 2009)

• LETKF: RTPP (Zhang et al. 2004)

• NEWS-e: adaptive inflation (Anderson 2009)– Trying to implement in LETKF

Similar obs diagnostics quality

Mean innovations and consistency ratios for 19 May 2013 analyses

VR DBZ

LETKF updates more balanced

Ensemble- and domain-averaged surface pressure tendencies for 19 May 2013 analyses

Storms move too fast in both!

LETKF EAKF

19 May 2013 FORECAST: 22-23 Z (3 h of DA)Shading: Neighborhood ensemble probability of dBZ > 35Contours: 35 dBZ MRMS (NSSL Multi-Radar Multi-Sensor) analysis at 22 Z and 23 Z

Surface temperature composites

LETKF EAKF

19 May 2013 FORECAST: Minimum 2-m T (C) during 21-22 Z (2 h DA)Shading: Ensemble mean Circles: Oklahoma mesonet obsContours: 40 dBZ MRMS at 21 Z

Rainfall composites

LETKF EAKF

20 May 2013 FORECAST: 20-21 Z (2 h DA)Shading: Neighborhood ensemble probability of 1-h rainfall > 0.5” Blue contours: NCEP Stage-4 rainfall > 0.5” Black contours: 40 dBZ MRMS at 20 Z

Vorticity composites

20 May 2013 FORECASTS: 1930-2030 (1.5 h DA)Shading: neighborhood ensemble probability ζ > .005 s-1 below 2 km AGLContours: 40 dBZ MRMS at initial time; tornado damage path (1956-2035 Z)Dots: NSSL rotation detections 1900-2200 Z (green = stronger)

LETKF EAKF

1930-2030 Z

Conclusion

• The WRF-LETKF and WRF-EAKF ensembles have similar accuracy

• This motivates exploration of the potentially superior computational scaling of LETKF

References

• Anderson, J. L., 2001: An ensemble adjustment filter for data assimilation. Mon. Wea. Rev., 129, 2884–2903.

• Anderson, J. L., 2009: Spatially and temporally varying adaptive covariance inflation for ensemble filters. Tellus, 61A, 72–83.

• Dowell, D. C., and L. J. Wicker, 2009: Additive noise for storm-scale ensemble forecasting and data assimilation. J. Atmos. Ocea. Tech. 26, 911–927.

• Holland, B., and X. Wang, 2013: Effects of sequential or simultaneous assimilation of observations and localization methods on the performance of the ensemble Kalman filter. Quart. J. Roy. Meteor. Soc., 139, 758–770.

• Hunt, B. R., E. J. Kostelich and I. Szunyogh, 2007: Efficient Data Assimilation of Spatiotemporal Chaos: A Local Ensemble Transform Kalman Filter. Physics D, 230, 112-126.

• Miyoshi, T., S. Yamane, 2007: Local Ensemble Transform Kalman Filtering with an AGCM at a T159/L48 Resolution. Mon. Wea. Rev., 135, 3841–3861.

• Nerger, L., 2015: On serial observation processing in localized ensemble Kalman filters. Mon. Wea. Rev., 143, 1554–1567.

• Stensrud, D. J., and Coauthors, 2009: Convective-scale warn-on-forecast system: A vision for 2020. Bull. Amer. Meteor. Soc., 90, 1487–1499.

• Zhang, F., C. Snyder, and J. Sun, 2004: Impacts of initial estimate and observation availability on convective-scale data assimilation with an ensemble Kalman filter. Mon. Wea. Rev., 132, 1238–1253.

• Zhao, G., 2013: Development of ARPS LETKF with Four Dimensional Extension and Intercomparison with ARPS EnSRF, School of Meteorology, University of Oklahoma, 200 pp.

Extra Slides

Warn-on-Forecast (cont.)

Stensrud et al. 2009, BAMS

Vorticity composites

LETKF EAKF

20 May 2013 FORECASTS: 2000-2100 (2.0 h DA)Shading: neighborhood ensemble probability ζ > .005 s-1 below 2 km AGLContours: 40 dBZ MRMS at initial time; tornado damage path (1956-2035 Z)Dots: NSSL rotation detections 1900-2200 Z (green = stronger)

2000-2100 Z

Vorticity composites

LETKF EAKF

20 May 2013 FORECASTS: 2030-2130 (2.5 h DA)Shading: neighborhood ensemble probability ζ > .005 s-1 below 2 km AGLContours: 40 dBZ MRMS at initial time; tornado damage path (1956-2035 Z)Dots: NSSL rotation detections 1900-2200 Z (green = stronger)

2030-2130 Z